Our proposed methods are evaluated on two segmentation tasks: (1) StructSeg 2019 challenge training dataset consisting of CT scans from 50 NPC patients where 22 OARs are to be segmented. (2) A mixed HAN CT dataset containing 165 volumes from three sources: 50 volumes from StructSeg 2019, 48 volumes from MICCAI 2015 Head and Neck challenge [1]} and 67 volumes collected locally with NPC before radiotherapy treatment, where we segment 7 OARs that are annotated in all of these three sources. For comparison, we implemented 3D-UNet [2]} with three versions: original one, SE block [3]} added and residual connection [4]} added, where we modified them by starting with 48 base channels and applying IN [5]} for normalization. These networks and our proposed 3D-SepNet were implemented in PyTorch, trained on two NVIDIA 1080TI GPUs for 250 epochs with the Adam optimizer [6]}, batch size 6, initial learning rate \(10^{-3}\)  and weight decay \(10^{-8}\) . The learning rate was decayed by 0.9 every 10 epochs. Without resampling, each CT slice was cropped by a \(256\times 256\)  window located at the center to focus on the body part. Random cropping of size \(16\times 128\times 128\)  was used for data augmentation. Our code is available at https://github.com/HiLab-git/SepNet.
